{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handwritten digits recognition (using Multilayer Perceptron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> - 🤖 See [full list of Machine Learning Experiments](https://github.com/trekhleb/machine-learning-experiments) on **GitHub**<br/><br/>\n",
    "> - ▶️ **Interactive Demo**: [try this model and other machine learning experiments in action](https://trekhleb.github.io/machine-learning-experiments/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this experiment we will build a [Multilayer Perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron) (MLP) model using [Tensorflow](https://www.tensorflow.org/) to recognize handwritten digits.\n",
    "\n",
    "A **multilayer perceptron** (MLP) is a class of feedforward artificial neural network. An MLP consists of, at least, three layers of nodes: an input layer, a hidden layer and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training. Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable.\n",
    "\n",
    "![digits_recognition_mlp.png](../../demos/src/images/digits_recognition_mlp.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import dependencies\n",
    "\n",
    "- [tensorflow](https://www.tensorflow.org/) - for developing and training ML models.\n",
    "- [matplotlib](https://matplotlib.org/) - for plotting the data.\n",
    "- [seaborn](https://seaborn.pydata.org/index.html) - for plotting confusion matrix.\n",
    "- [numpy](https://numpy.org/) - for linear algebra operations.\n",
    "- [pandas](https://pandas.pydata.org/) - for displaying training/test data in a table.\n",
    "- [math](https://docs.python.org/3/library/math.html) - for calculating square roots etc.\n",
    "- [datetime](https://docs.python.org/3.8/library/datetime.html) - for generating a logs folder names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting Tensorflow version v2 (the command is relevant for Colab only).\n",
    "%tensorflow_version 2.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 3.7.6\n",
      "Tensorflow version: 2.1.0\n",
      "Keras version: 2.2.4-tf\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import datetime\n",
    "import platform\n",
    "\n",
    "print('Python version:', platform.python_version())\n",
    "print('Tensorflow version:', tf.__version__)\n",
    "print('Keras version:', tf.keras.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuring Tensorboard\n",
    "\n",
    "We will use [Tensorboard](https://www.tensorflow.org/tensorboard) to debug the model later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the TensorBoard notebook extension.\n",
    "# %reload_ext tensorboard\n",
    "%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clear any logs from previous runs.\n",
    "!rm -rf ./.logs/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data\n",
    "\n",
    "The **training** dataset consists of 60000 28x28px images of hand-written digits from `0` to `9`.\n",
    "\n",
    "The **test** dataset consists of 10000 28x28px images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_dataset = tf.keras.datasets.mnist\n",
    "(x_train, y_train), (x_test, y_test) = mnist_dataset.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train: (60000, 28, 28)\n",
      "y_train: (60000,)\n",
      "x_test: (10000, 28, 28)\n",
      "y_test: (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('x_train:', x_train.shape)\n",
    "print('y_train:', y_train.shape)\n",
    "print('x_test:', x_test.shape)\n",
    "print('y_test:', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the data\n",
    "\n",
    "Here is how each image in the dataset looks like. It is a 28x28 matrix of integers (from `0` to `255`). Each integer represents a color of a pixel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>253</td>\n",
       "      <td>249</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>253</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>250</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>171</td>\n",
       "      <td>219</td>\n",
       "      <td>253</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>172</td>\n",
       "      <td>226</td>\n",
       "      <td>253</td>\n",
       "      <td>253</td>\n",
       "      <td>253</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>253</td>\n",
       "      <td>253</td>\n",
       "      <td>253</td>\n",
       "      <td>212</td>\n",
       "      <td>135</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3    4    5    6    7    8    9   ...   18   19   20   21  \\\n",
       "0    0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "1    0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "2    0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "3    0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "4    0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "5    0   0   0   0    0    0    0    0    0    0  ...  175   26  166  255   \n",
       "6    0   0   0   0    0    0    0    0   30   36  ...  225  172  253  242   \n",
       "7    0   0   0   0    0    0    0   49  238  253  ...   93   82   82   56   \n",
       "8    0   0   0   0    0    0    0   18  219  253  ...    0    0    0    0   \n",
       "9    0   0   0   0    0    0    0    0   80  156  ...    0    0    0    0   \n",
       "10   0   0   0   0    0    0    0    0    0   14  ...    0    0    0    0   \n",
       "11   0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "12   0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "13   0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "14   0   0   0   0    0    0    0    0    0    0  ...   25    0    0    0   \n",
       "15   0   0   0   0    0    0    0    0    0    0  ...  150   27    0    0   \n",
       "16   0   0   0   0    0    0    0    0    0    0  ...  253  187    0    0   \n",
       "17   0   0   0   0    0    0    0    0    0    0  ...  253  249   64    0   \n",
       "18   0   0   0   0    0    0    0    0    0    0  ...  253  207    2    0   \n",
       "19   0   0   0   0    0    0    0    0    0    0  ...  250  182    0    0   \n",
       "20   0   0   0   0    0    0    0    0    0    0  ...   78    0    0    0   \n",
       "21   0   0   0   0    0    0    0    0   23   66  ...    0    0    0    0   \n",
       "22   0   0   0   0    0    0   18  171  219  253  ...    0    0    0    0   \n",
       "23   0   0   0   0   55  172  226  253  253  253  ...    0    0    0    0   \n",
       "24   0   0   0   0  136  253  253  253  212  135  ...    0    0    0    0   \n",
       "25   0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "26   0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "27   0   0   0   0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "\n",
       "     22   23  24  25  26  27  \n",
       "0     0    0   0   0   0   0  \n",
       "1     0    0   0   0   0   0  \n",
       "2     0    0   0   0   0   0  \n",
       "3     0    0   0   0   0   0  \n",
       "4     0    0   0   0   0   0  \n",
       "5   247  127   0   0   0   0  \n",
       "6   195   64   0   0   0   0  \n",
       "7    39    0   0   0   0   0  \n",
       "8     0    0   0   0   0   0  \n",
       "9     0    0   0   0   0   0  \n",
       "10    0    0   0   0   0   0  \n",
       "11    0    0   0   0   0   0  \n",
       "12    0    0   0   0   0   0  \n",
       "13    0    0   0   0   0   0  \n",
       "14    0    0   0   0   0   0  \n",
       "15    0    0   0   0   0   0  \n",
       "16    0    0   0   0   0   0  \n",
       "17    0    0   0   0   0   0  \n",
       "18    0    0   0   0   0   0  \n",
       "19    0    0   0   0   0   0  \n",
       "20    0    0   0   0   0   0  \n",
       "21    0    0   0   0   0   0  \n",
       "22    0    0   0   0   0   0  \n",
       "23    0    0   0   0   0   0  \n",
       "24    0    0   0   0   0   0  \n",
       "25    0    0   0   0   0   0  \n",
       "26    0    0   0   0   0   0  \n",
       "27    0    0   0   0   0   0  \n",
       "\n",
       "[28 rows x 28 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(x_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This matrix of numbers may be drawn as follows: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[0], cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print some more training examples to get the feeling of how the digits were written."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numbers_to_display = 25\n",
    "num_cells = math.ceil(math.sqrt(numbers_to_display))\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(numbers_to_display):\n",
    "    plt.subplot(num_cells, num_cells, i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(x_train[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(y_train[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize the data\n",
    "\n",
    "Here we're just trying to move from values range of `[0...255]` to `[0...1]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_normalized = x_train / 255\n",
    "x_test_normalized = x_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.14</td>\n",
       "      <td>...</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.99</td>\n",
       "      <td>...</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.99</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.61</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.26</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.99</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.53</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4    5    6    7    8    9   ...   18   19   20   21  \\\n",
       "0  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.00 0.00 0.00 0.00   \n",
       "1  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.00 0.00 0.00 0.00   \n",
       "2  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.00 0.00 0.00 0.00   \n",
       "3  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.00 0.00 0.00 0.00   \n",
       "4  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.00 0.00 0.00 0.00   \n",
       "5  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.69 0.10 0.65 1.00   \n",
       "6  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.14  ... 0.88 0.67 0.99 0.95   \n",
       "7  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.19 0.93 0.99  ... 0.36 0.32 0.32 0.22   \n",
       "8  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.86 0.99  ... 0.00 0.00 0.00 0.00   \n",
       "9  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.31 0.61  ... 0.00 0.00 0.00 0.00   \n",
       "10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05  ... 0.00 0.00 0.00 0.00   \n",
       "11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.00 0.00 0.00 0.00   \n",
       "12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.00 0.00 0.00 0.00   \n",
       "13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.00 0.00 0.00 0.00   \n",
       "14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.10 0.00 0.00 0.00   \n",
       "15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.59 0.11 0.00 0.00   \n",
       "16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.99 0.73 0.00 0.00   \n",
       "17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.99 0.98 0.25 0.00   \n",
       "18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.99 0.81 0.01 0.00   \n",
       "19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.98 0.71 0.00 0.00   \n",
       "20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.31 0.00 0.00 0.00   \n",
       "21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.26  ... 0.00 0.00 0.00 0.00   \n",
       "22 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.67 0.86 0.99  ... 0.00 0.00 0.00 0.00   \n",
       "23 0.00 0.00 0.00 0.00 0.22 0.67 0.89 0.99 0.99 0.99  ... 0.00 0.00 0.00 0.00   \n",
       "24 0.00 0.00 0.00 0.00 0.53 0.99 0.99 0.99 0.83 0.53  ... 0.00 0.00 0.00 0.00   \n",
       "25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.00 0.00 0.00 0.00   \n",
       "26 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.00 0.00 0.00 0.00   \n",
       "27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00  ... 0.00 0.00 0.00 0.00   \n",
       "\n",
       "     22   23   24   25   26   27  \n",
       "0  0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "1  0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "2  0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "3  0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "4  0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "5  0.97 0.50 0.00 0.00 0.00 0.00  \n",
       "6  0.76 0.25 0.00 0.00 0.00 0.00  \n",
       "7  0.15 0.00 0.00 0.00 0.00 0.00  \n",
       "8  0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "9  0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "10 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "11 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "12 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "13 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "14 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "15 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "16 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "17 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "18 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "19 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "20 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "21 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "22 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "23 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "24 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "25 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "26 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "27 0.00 0.00 0.00 0.00 0.00 0.00  \n",
       "\n",
       "[28 rows x 28 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context('display.float_format', '{:,.2f}'.format):\n",
    "    display(pd.DataFrame(x_train_normalized[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how the digits look like after normalization. We're expecting it to look similar to original."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xOgua7Rb1PUXvkbQv+7i76McuMVdLHjd+XRYIghfogCAoOxAEZQeCoOxAEJQdCIKyA0FQdiCI/wfvpjt5Q0mdXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train_normalized[0], cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the model\n",
    "\n",
    "We will use [Sequential](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential?version=stable) Keras model with 4 layers:\n",
    "\n",
    "- Layer 1: [Flatten](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Flatten?version=stable) layer that will flatten image 2D matrix into 1D vector.\n",
    "- Layer 2: **Input** [Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense?version=stable) layer with `128` neurons and [ReLU](https://www.tensorflow.org/api_docs/python/tf/keras/activations/relu?version=stable) activation.\n",
    "- Layer 3: **Hidden** [Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense?version=stable) layer with `128` neurons and [ReLU](https://www.tensorflow.org/api_docs/python/tf/keras/activations/relu?version=stable) activation.\n",
    "- Layer 4: **Output** [Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense?version=stable) layer with `10` [Softmax](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax?version=stable) outputs. The output represents the network guess. The 0-th output represents a probability that the input digit is `0`, the 1-st output represents a probability that the input digit is `1` and so on...\n",
    "\n",
    "In this example we will use `kernel_regularizer` parameter of the layer to control overfitting of the model. Another common approach to fight overfitting though might be using a [dropout layers](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout) (i.e. `tf.keras.layers.Dropout(0.2)`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential()\n",
    "\n",
    "# Input layers.\n",
    "model.add(tf.keras.layers.Flatten(input_shape=x_train_normalized.shape[1:]))\n",
    "model.add(tf.keras.layers.Dense(\n",
    "    units=128,\n",
    "    activation=tf.keras.activations.relu,\n",
    "    kernel_regularizer=tf.keras.regularizers.l2(0.002)\n",
    "))\n",
    "\n",
    "# Hidden layers.\n",
    "model.add(tf.keras.layers.Dense(\n",
    "    units=128,\n",
    "    activation=tf.keras.activations.relu,\n",
    "    kernel_regularizer=tf.keras.regularizers.l2(0.002)\n",
    "))\n",
    "\n",
    "# Output layers.\n",
    "model.add(tf.keras.layers.Dense(\n",
    "    units=10,\n",
    "    activation=tf.keras.activations.softmax\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is our model summary so far."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 118,282\n",
      "Trainable params: 118,282\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to plot the model the `graphviz` should be installed. For Mac OS it may be installed using `brew` like `brew install graphviz`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(\n",
    "    model,\n",
    "    show_shapes=True,\n",
    "    show_layer_names=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compile the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "adam_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=adam_optimizer,\n",
    "    loss=tf.keras.losses.sparse_categorical_crossentropy,\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 10s 173us/sample - loss: 0.5159 - accuracy: 0.9247 - val_loss: 0.3251 - val_accuracy: 0.9512\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 12s 195us/sample - loss: 0.2996 - accuracy: 0.9546 - val_loss: 0.2767 - val_accuracy: 0.9558\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 11s 180us/sample - loss: 0.2624 - accuracy: 0.9600 - val_loss: 0.2342 - val_accuracy: 0.9675\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 10s 174us/sample - loss: 0.2408 - accuracy: 0.9629 - val_loss: 0.2276 - val_accuracy: 0.9650\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 8s 138us/sample - loss: 0.2257 - accuracy: 0.9645 - val_loss: 0.2415 - val_accuracy: 0.9584\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 8s 139us/sample - loss: 0.2148 - accuracy: 0.9669 - val_loss: 0.2188 - val_accuracy: 0.9639\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 10s 164us/sample - loss: 0.2044 - accuracy: 0.9689 - val_loss: 0.1923 - val_accuracy: 0.9707\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 8s 139us/sample - loss: 0.1980 - accuracy: 0.9688 - val_loss: 0.2008 - val_accuracy: 0.9662\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 9s 146us/sample - loss: 0.1940 - accuracy: 0.9685 - val_loss: 0.2088 - val_accuracy: 0.9641\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 7s 116us/sample - loss: 0.1906 - accuracy: 0.9693 - val_loss: 0.2004 - val_accuracy: 0.9646\n"
     ]
    }
   ],
   "source": [
    "log_dir=\".logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
    "\n",
    "training_history = model.fit(\n",
    "    x_train_normalized,\n",
    "    y_train,\n",
    "    epochs=10,\n",
    "    validation_data=(x_test_normalized, y_test),\n",
    "    callbacks=[tensorboard_callback]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how the loss function was changing during the training. We expect it to get smaller and smaller on every next epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1406d4750>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Epoch Number')\n",
    "plt.ylabel('Loss')\n",
    "plt.plot(training_history.history['loss'], label='training set')\n",
    "plt.plot(training_history.history['val_loss'], label='test set')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14069cbd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Epoch Number')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.plot(training_history.history['accuracy'], label='training set')\n",
    "plt.plot(training_history.history['val_accuracy'], label='test set')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate model accuracy\n",
    "\n",
    "We need to compare the accuracy of our model on **training** set and on **test** set. We expect our model to perform similarly on both sets. If the performance on a test set will be poor comparing to a training set it would be an indicator for us that the model is overfitted and we have a \"high variance\" issue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training set accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "train_loss, train_accuracy = model.evaluate(x_train_normalized, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss:  0.1835839938680331\n",
      "Training accuracy:  0.97095\n"
     ]
    }
   ],
   "source": [
    "print('Training loss: ', train_loss)\n",
    "print('Training accuracy: ', train_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test set accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "validation_loss, validation_accuracy = model.evaluate(x_test_normalized, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation loss:  0.2004156953573227\n",
      "Validation accuracy:  0.9646\n"
     ]
    }
   ],
   "source": [
    "print('Validation loss: ', validation_loss)\n",
    "print('Validation accuracy: ', validation_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the model\n",
    "\n",
    "We will save the entire model to a `HDF5` file. The `.h5` extension of the file indicates that the model should be saved in Keras format as HDF5 file. To use this model on the front-end we will convert it (later in this notebook) to Javascript understandable format (`tfjs_layers_model` with .json and .bin files) using [tensorflowjs_converter](https://www.tensorflow.org/js/tutorials/conversion/import_saved_model) as it is specified in the [main README](https://github.com/trekhleb/machine-learning-experiments)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'digits_recognition_mlp.h5'\n",
    "model.save(model_name, save_format='h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = tf.keras.models.load_model(model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use the model (do predictions)\n",
    "\n",
    "To use the model that we've just trained for digits recognition we need to call `predict()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_one_hot = loaded_model.predict([x_test_normalized])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions_one_hot: (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print('predictions_one_hot:', predictions_one_hot.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each prediction consists of 10 probabilities (one for each number from `0` to `9`). We need to pick the digit with the highest probability since this would be a digit that our model most confident with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.167473e-03</td>\n",
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       "      <td>8.854911e-04</td>\n",
       "      <td>6.828848e-04</td>\n",
       "      <td>5.048555e-06</td>\n",
       "      <td>5.102141e-03</td>\n",
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       "      <td>2.035172e-04</td>\n",
       "      <td>9.576474e-05</td>\n",
       "      <td>5.053744e-04</td>\n",
       "      <td>5.977653e-06</td>\n",
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       "      <th>3</th>\n",
       "      <td>9.990014e-01</td>\n",
       "      <td>4.625264e-06</td>\n",
       "      <td>5.582303e-04</td>\n",
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       "      <td>8.899846e-05</td>\n",
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       "      <td>4.155232e-05</td>\n",
       "      <td>4.639028e-06</td>\n",
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       "      <td>2.244577e-04</td>\n",
       "      <td>6.904386e-12</td>\n",
       "      <td>1.850143e-07</td>\n",
       "      <td>4.015289e-08</td>\n",
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       "      <td>6.165197e-06</td>\n",
       "      <td>9.982822e-01</td>\n",
       "      <td>2.519031e-09</td>\n",
       "      <td>1.577783e-03</td>\n",
       "      <td>5.583775e-11</td>\n",
       "      <td>2.066899e-06</td>\n",
       "      <td>1.257137e-05</td>\n",
       "      <td>1.125286e-04</td>\n",
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       "      <td>3.161353e-09</td>\n",
       "      <td>6.484316e-08</td>\n",
       "      <td>9.989114e-01</td>\n",
       "      <td>2.373860e-07</td>\n",
       "      <td>1.930965e-08</td>\n",
       "      <td>1.753431e-05</td>\n",
       "      <td>5.452521e-06</td>\n",
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       "      <td>1.025373e-04</td>\n",
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       "      <td>1.444746e-07</td>\n",
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       "      <td>1.321389e-07</td>\n",
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       "      <td>2.270459e-07</td>\n",
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       "                 0             1             2             3             4  \\\n",
       "0     7.774682e-07  1.361266e-05  9.182121e-05  1.480533e-04  3.271606e-08   \n",
       "1     1.198126e-03  1.047034e-04  9.888538e-01  3.167473e-03  2.532723e-08   \n",
       "2     8.876222e-07  9.985157e-01  7.702865e-05  2.815677e-05  5.406159e-04   \n",
       "3     9.990014e-01  4.625264e-06  5.582303e-04  5.484722e-06  3.299095e-05   \n",
       "4     1.575061e-04  3.707617e-06  9.205778e-06  3.638557e-07  9.973990e-01   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "9995  1.657425e-07  8.666945e-04  9.987835e-01  2.244577e-04  6.904386e-12   \n",
       "9996  6.585806e-09  6.717554e-06  6.165197e-06  9.982822e-01  2.519031e-09   \n",
       "9997  3.056851e-08  6.843247e-06  3.161353e-09  6.484316e-08  9.989114e-01   \n",
       "9998  7.249156e-06  5.103301e-07  2.712475e-08  1.025373e-04  5.019490e-08   \n",
       "9999  2.355737e-06  4.141651e-07  4.489176e-06  1.321389e-07  2.956528e-05   \n",
       "\n",
       "                 5             6             7             8             9  \n",
       "0     2.764984e-06  5.903113e-11  9.996371e-01  1.666906e-06  1.042360e-04  \n",
       "1     8.854911e-04  6.828848e-04  5.048555e-06  5.102141e-03  2.777219e-07  \n",
       "2     2.707353e-05  2.035172e-04  9.576474e-05  5.053744e-04  5.977653e-06  \n",
       "3     2.761683e-05  1.418936e-04  1.374896e-04  1.264711e-06  8.899846e-05  \n",
       "4     1.538193e-06  3.079933e-05  4.155232e-05  4.639028e-06  2.351647e-03  \n",
       "...            ...           ...           ...           ...           ...  \n",
       "9995  1.850143e-07  4.015289e-08  9.534077e-05  2.960863e-05  4.335253e-10  \n",
       "9996  1.577783e-03  5.583775e-11  2.066899e-06  1.257137e-05  1.125286e-04  \n",
       "9997  2.373860e-07  1.930965e-08  1.753431e-05  5.452521e-06  1.058474e-03  \n",
       "9998  9.996431e-01  9.364716e-05  1.444746e-07  1.520906e-04  6.703385e-07  \n",
       "9999  3.940167e-05  9.999231e-01  7.314535e-08  2.270459e-07  1.044889e-07  \n",
       "\n",
       "[10000 rows x 10 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predictions in form of one-hot vectors (arrays of probabilities).\n",
    "pd.DataFrame(predictions_one_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>7</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <th>...</th>\n",
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       "      <th>9995</th>\n",
       "      <td>2</td>\n",
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       "      <th>9996</th>\n",
       "      <td>3</td>\n",
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       "      <th>9997</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0\n",
       "0     7\n",
       "1     2\n",
       "2     1\n",
       "3     0\n",
       "4     4\n",
       "...  ..\n",
       "9995  2\n",
       "9996  3\n",
       "9997  4\n",
       "9998  5\n",
       "9999  6\n",
       "\n",
       "[10000 rows x 1 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's extract predictions with highest probabilites and detect what digits have been actually recognized.\n",
    "predictions = np.argmax(predictions_one_hot, axis=1)\n",
    "pd.DataFrame(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So our model is predicting that the first example from the test set is `7`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "print(predictions[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print the first image from a test set to see if model's prediction is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_test_normalized[0], cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that our model made a correct prediction and it successfully recognized digit `7`. Let's print some more test examples and correspondent predictions to see how model performs and where it does mistakes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 196 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numbers_to_display = 196\n",
    "num_cells = math.ceil(math.sqrt(numbers_to_display))\n",
    "plt.figure(figsize=(15, 15))\n",
    "\n",
    "for plot_index in range(numbers_to_display):    \n",
    "    predicted_label = predictions[plot_index]\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    color_map = 'Greens' if predicted_label == y_test[plot_index] else 'Reds'\n",
    "    plt.subplot(num_cells, num_cells, plot_index + 1)\n",
    "    plt.imshow(x_test_normalized[plot_index], cmap=color_map)\n",
    "    plt.xlabel(predicted_label)\n",
    "\n",
    "plt.subplots_adjust(hspace=1, wspace=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a confusion matrix\n",
    "\n",
    "[Confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix) shows what numbers are recognized well by the model and what numbers the model usually confuses to recognize correctly. You may see that the model performs really well but sometimes (28 times out of 10000) it may confuse number `5` with `3` or number `2` with `3`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = tf.math.confusion_matrix(y_test, predictions)\n",
    "f, ax = plt.subplots(figsize=(9, 7))\n",
    "sn.heatmap(\n",
    "    confusion_matrix,\n",
    "    annot=True,\n",
    "    linewidths=.5,\n",
    "    fmt=\"d\",\n",
    "    square=True,\n",
    "    ax=ax\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Debugging the model with TensorBoard\n",
    "\n",
    "[TensorBoard](https://www.tensorflow.org/tensorboard) is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/sh: line 0: kill: (43638) - No such process\r\n"
     ]
    }
   ],
   "source": [
    "%tensorboard --logdir .logs/fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting the model to web-format\n",
    "\n",
    "To use this model on the web we need to convert it into the format that will be understandable by [tensorflowjs](https://www.tensorflow.org/js). To do so we may use [tfjs-converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter) as following:\n",
    "\n",
    "```\n",
    "tensorflowjs_converter --input_format keras \\\n",
    "  ./experiments/digits_recognition_mlp/digits_recognition_mlp.h5 \\\n",
    "  ./demos/public/models/digits_recognition_mlp\n",
    "```\n",
    "\n",
    "You find this experiment in the [Demo app](https://trekhleb.github.io/machine-learning-experiments) and play around with it right in you browser to see how the model performs in real life."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
